A Comparison of Customer Churn Vector Embedding Models with Deep Learning

Abstract

In the telecommunication industry, deep learning
has been utilized for churn prediction. Some companies have
used sophisticated deep learning techniques to predict churn,
which yielded satisfactory results. However, future studies are
still required to evaluate several deep learning mechanisms as
only SoftMax Loss has been used so far. By comparing
customer churn vector embedding models with several
methods, including SoftMax Loss, Large Margin Cosine Loss,
Semi-Supervised Learning, and a combination of Large
Margin Cosine Loss and Semi-Supervised Learning. The use of
Large Margin Cosine Loss has been proven in face recognition
which can increase the discrimination between vectors
embedding in different classes. Understanding how mixing
unlabeled and labeled input might alter developing algorithms
and learning behavior that benefit from this combination are
the goals of semi-supervised learning. This approach
encouraged feature discrimination in customer behavior and
improved the model's overall accuracy. Large Margin Cosine
Loss in this study achieved 83.74% of the F1 Score compared
to other methods. It was further demonstrated that the
produced vectors for churn prediction are discriminative by
examining the cluster's similarity and the t-SNE plot. The tSNE visualization showed that the proposed model produces
highly discriminative vectors with the Large Margin Cosine
Loss model embedded vector being thicker than SoftMax Loss,
Semi-Supervised Learning, and a combination of Large
Margin Cosine Loss and Semi-Supervised Learning model
churn clusters